Quick guide to genetic analysis for mixed models with WOMBAT

Authors

DOI:

https://doi.org/10.71112/pw47g958

Keywords:

WOMBAT, genetic value, animal model, mixed model, REML

Abstract

The identification of the best individuals is based on obtaining the genetic values ​​(GV), or best linear unbiased predictors (BLUP) as they are also known, which allow the identification of genetically superior or inferior animals in the herd. A computer program widely used worldwide to calculate the GV is WOMBAT, which was written in FORTRAN95 by Dr. Karin Meyer and launched on the market in 2005. WOMBAT uses the methodology of mixed linear models under the REML method. A database of weaning weight in cattle was used to show the use of WOMBAT in calculating the GV using 3 different models. For the 3 models different results were found for the GV. It can be concluded that WOMBAT-REML can predict the GV of animals for the characteristics using efficient algorithms under mixed models, its use is recommended for the genetic evaluation of characteristics of zootechnical interest in animal populations.

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References

Aranguren, A., & Román, R. (2014). El modelo animal simple: una metodología para los genetistas. Logros & Desafíos de la Ganadería Doble Propósito, GIRAZ, 120-136.

Aranguren, A., Román, R., Villasmil, Y., & Yañez, F. (2007). Evaluación genética de la ganadería mestiza doble propósito en Venezuela. Archivos Latinoamericanos de Producción Animal, 15(1), 241-250.

Becker, W. (1986). Manual de genética cuantitativa. Academic Enterprises.

Blasco, A. (2017). Bayesian data analysis for animal scientists: The basics. Springer. DOI: https://doi.org/10.1007/978-3-319-54274-4

Blasco, A. (2021). Mejora genética animal. Editorial Síntesis.

Boldman, K. G., Kriese, L. A., Van Vleck, L. D., Van Tassell, C. P., & Kachman, S. D. (1995). A manual for use of MTDFREML: A set of programs to obtain estimates of variances and covariances [Draft]. U.S. Department of Agriculture, Agricultural Research Service.

Castejón, O. (2008). Diseño y análisis de experimentos con Statistix. Colección de Textos Universitarios. Ediciones del Vicerrectorado Académico.

Elzo, M., & Garay, O. (2012). Modelación aplicada a las ciencias animales: II. Evaluaciones genéticas. Editorial Biogénesis.

Falconer, D. (2001). Introducción a la genética cuantitativa. Longman.

Fernández, N., Herrera, J. C., Pérez, N. G., Doria, M. R., Mestra, L. V., & Lucero, C. (2021). Heredabilidades para características de crecimiento a través de los años en la raza Blanco Orejinegro. Revista de Investigaciones Veterinarias del Perú, 32(5). DOI: https://doi.org/10.15381/rivep.v32i5.19294

Gilmour, A. R. (2021). Echidna Mixed Model Software. Recuperado de www.EchidnaMMS.org.

Gutiérrez, P. (2010). Iniciación a la valoración genética animal: Metodología adaptada al EEES. Editorial Complutense.

Henderson, C. (1953). Estimation of variance and covariance components. Biometrics, 9(2), 226-252. DOI: https://doi.org/10.2307/3001853

Henderson, C. (1973). Sire evaluation and genetic trends. En Proceedings of the Animal Breeding Genetics Symposium in Honor of J.L. Lush (pp. 10-41). American Society of Animal Science. https://doi.org/10.1093/ansci/1973.Symposium.10 DOI: https://doi.org/10.1093/ansci/1973.Symposium.10

Johnson, D., & Thompson, R. (1995). Restricted maximum likelihood estimation of variance components for univariate animal models using sparse matrix techniques and average information. Journal of Dairy Science, 78, 449-456. DOI: https://doi.org/10.3168/jds.S0022-0302(95)76654-1

Legates, E., & Warwick, J. (1992). Cría y mejora del ganado. Interamericana McGraw-Hill.

Littell, R., Milliken, G., Stroup, W., Wolfinger, R., & Schabenberger, O. (2006). SAS for mixed models. SAS Press.

Lourenco, D., Tsuruta, S., Masuda, Y., Bermann, M., Legarra, A., & Misztal, I. (2022). Actualizaciones recientes en el paquete de software BLUPF90. Congreso Mundial de Genética Aplicada a la Producción Ganadera.

Meyer, K. (1989). Restricted maximum likelihood to estimate variance components for animal models with several random effects using a derivative-free algorithm. Genetics Selection Evolution, 21, 317-340. DOI: https://doi.org/10.1051/gse:19890308

Meyer, K. (1997). An ‘average information’ restricted maximum likelihood algorithm for estimating reduced rank genetic matrices or covariance functions for animal models with equal design matrices. Genetics Selection Evolution, 29, 97-116. DOI: https://doi.org/10.1051/gse:19970201

Meyer, K. (2007). WOMBAT: A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B, 8(11), 815-821. DOI: https://doi.org/10.1631/jzus.2007.B0815

Misztal, I., Tsuruta, S., Lourenco, D. A. L., Aguilar, I., Legarra, A., & Vitezica, Z. (2014). Manual para la familia de programas BLUPF90.

Mrode, R., & Thompson, P. (2005). Linear models for the prediction of animal breeding values (2ª ed.). CABI Publishing. DOI: https://doi.org/10.1079/9780851990002.0000

Patterson, H., & Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika, 58, 545-554. DOI: https://doi.org/10.1093/biomet/58.3.545

Pérez, J. (2024). Estadística aplicada al mejoramiento genético animal. Fondo Editorial Universidad Rafael Urdaneta.

Pérez, J., Jiménez, E., & Morales, D. (2024). Repetibilidad del intervalo entre parto en ganado Carora en Venezuela. RECITIUTM, 10(2).

Pérez, J., & Morales, D. (2023). Theory of estimation of parameters and genetic values under mixed models. International Journal of Avian & Wildlife Biology, 8(1), 27-33. https://doi.org/10.15406/ijawb.2024.08.00210 DOI: https://doi.org/10.15406/ijawb.2024.08.00210

Román, R., & Aranguren, A. (2014). Evaluación genética de reproductores: Logros y desafíos. GIRAZ.

Searle, S. R., Casella, G., & McCulloch, C. E. (1992). Variance components. Wiley. DOI: https://doi.org/10.1002/9780470316856

Solarte, C., Martínez, C., & Cerón, M. (2024). Modelos lineales para evaluación genética en animales. Editorial UTP.

Sorensen, D., & Gianola, D. (2002). Likelihood, Bayesian, and MCMC methods in quantitative genetics. Springer. DOI: https://doi.org/10.1007/b98952

Vega, P. (1998). Introducción a la teoría de genética cuantitativa con especial referencia al mejoramiento de plantas. UCV-Ediciones de la Biblioteca.

Verde, O., & Yañez, F. (2014). Modelos estadísticos de evaluación genética. Logros & Desafíos de la Ganadería Doble Propósito, GIRAZ, 107-119.

Vilela, J. (2014). Mejoramiento genético animal en animales domésticos. Editorial Macro. Lima, Perú.

Published

2025-01-28

Issue

Section

Computational Sciences

How to Cite

Quick guide to genetic analysis for mixed models with WOMBAT. (2025). Multidisciplinary Journal Epistemology of the Sciences, 2(1), 19-39. https://doi.org/10.71112/pw47g958

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